.ipynb .pdf

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Binder

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CO2 emissions and climate change

The past, present and future responsibilities

import pandas as pd 
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
import seaborn as sns
import pycountry_convert as pc
import plotly.express as px
import plotly.offline as pyo
import plotly.express as px
import plotly.io as pio
import numpy as np
#pyo.init_notebook_mode()

pio.renderers.default = 'notebook'

def country_to_continent(country_code):
    try:
        country_alpha2 = pc.country_alpha3_to_country_alpha2(country_code)
        country_continent_code = pc.country_alpha2_to_continent_code(country_alpha2)
        country_continent_name = pc.convert_continent_code_to_continent_name(country_continent_code)
    except: 
        country_continent_name = country_code
        
    return country_continent_name

df = pd.read_csv('data/owid-co2-data.csv',sep = ',',encoding = 'unicode_escape')
df = df.fillna(0)
temp = df[['iso_code', 'country', 'year', 'co2','energy_per_gdp', 'co2_per_capita','primary_energy_consumption','gdp','co2_per_gdp']] 

df_sector = pd.read_csv('data/co-emissions-by-sector.csv',sep = ',',encoding = 'unicode_escape')
df_sector = df_sector.fillna(0)
df_sector['Continent'] = df_sector['Code'].apply(lambda x: country_to_continent(x))

len(set(df.country))

243

df_sector_18 = df_sector[(df_sector['Year'] == 2018) & ~(df_sector['Continent'].isin([0,'TLS','OWID_WRL']))]
df_sun = df_sector_18[~df_sector_18[['Buildings', 'Industry',
       'Land-use change and forestry', 'Other fuel combustion', 'Transport',
       'Manufacturing and construction', 'Fugitive emissions',
       'Electricity and heat']].eq(0).all(axis = 1)].drop(columns = ["Year","Code","Land-use change and forestry"])

df_sun2 = df_sun.melt(id_vars=["Entity","Continent"], 
        var_name="Sector", 
        value_name="Value")
#temp = df_sun2[df_sun2['Entity'].isin(['Mongolia'])]
#temp
fig = px.sunburst(df_sun2, path=['Continent', 'Entity','Sector'], values='Value', color='Continent',width=1000, height=800)
fig.update_traces(textinfo="label+percent entry ")
fig.show()

mask = (temp['year'] > 1990) & (temp['country'].isin(['Asia','Europe','Oceania','Africa',
                                                      'North America','South America','Antarctica']))

mask2 = (temp['year'] > 1990) & ~(temp['country'].isin(['Asia','Europe','Oceania','Africa',
                                                      'North America','South America','Antarctica','World']))

mask3 = (temp['year'] > 1990) & (temp['country'].isin(['China','United States','Canada','Qatar',
                                                      'India','Germany','United Kingdom','Saudi Arabia']))

cont_co2 = temp[mask2]
cont_co22 = temp[mask]

fig = px.choropleth(cont_co2, locations="iso_code", color="co2", hover_name="country", animation_frame="year",
                    range_color=[np.percentile(cont_co2['co2'],5),max(cont_co2['co2'])],
                    projection = 'natural earth',
                    title="Total CO2 emission per country from 1990 to 2020",labels={'co2':'million tonnes'})
fig.layout.updatemenus[0].buttons[0].args[1]['frame']['duration'] = 150
fig.layout.updatemenus[0].buttons[0].args[1]['transition']['duration'] = 10
fig.show()

fig = px.choropleth(cont_co2, locations="iso_code", color="co2_per_capita", hover_name="country", animation_frame="year",
                    range_color=[np.percentile(cont_co2['co2_per_capita'],1),max(cont_co2['co2_per_capita'])],
                    projection='natural earth',
                    title="CO2 emission per capita per country from 1990 to 2020",labels={'co2_per_capita':'tonnes'})
fig.layout.updatemenus[0].buttons[0].args[1]['frame']['duration'] = 150
fig.layout.updatemenus[0].buttons[0].args[1]['transition']['duration'] = 10
fig.show()

temp = cont_co2[cont_co2['country'].isin(['China','India','United States','Germany','Saudi Arabia'])]
fig = px.scatter(temp, x="primary_energy_consumption", y="co2", color='country', trendline="ols")
fig.show()

df2 = px.data.gapminder()
fig = px.scatter(temp, x="primary_energy_consumption", y="co2", animation_frame="year", animation_group="country",
           size="co2_per_capita", color="country", hover_name="country", facet_col="country",labels={'country':'Country'},
           log_x=False, log_y= False, size_max=45, range_x=[1,max(temp['energy_per_gdp'])], range_y=[1,max(temp['co2_per_capita'])])

fig.layout.updatemenus[0].buttons[0].args[1]['frame']['duration'] = 150
fig.layout.updatemenus[0].buttons[0].args[1]['transition']['duration'] = 10
fig.show()

df.describe()

	year	co2	co2_per_capita	trade_co2	cement_co2	cement_co2_per_capita	coal_co2	coal_co2_per_capita	flaring_co2	flaring_co2_per_capita	...	ghg_excluding_lucf_per_capita	methane	methane_per_capita	nitrous_oxide	nitrous_oxide_per_capita	population	gdp	primary_energy_consumption	energy_per_capita	energy_per_gdp
count	25191.000000	25191.000000	25191.000000	25191.000000	25191.000000	25191.000000	25191.000000	25191.000000	25191.000000	25191.000000	...	25191.000000	25191.000000	25191.000000	25191.000000	25191.000000	2.519100e+04	2.519100e+04	25191.000000	25191.000000	25191.000000
mean	1953.195546	254.655458	3.858882	-0.381268	6.266510	0.054825	119.648138	1.038412	2.609322	0.152228	...	1.541270	17.750555	0.427005	6.529960	0.135134	6.422993e+07	1.546188e+11	541.278196	8811.084895	0.499684
std	53.788944	1484.826244	14.385611	72.480032	54.253863	0.118091	654.445808	2.211787	17.810338	2.308195	...	4.479964	260.308638	1.761079	94.657017	0.490859	3.623151e+08	1.604593e+12	5376.643859	23026.452190	1.159689
min	1750.000000	0.000000	0.000000	-1657.998000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000e+00	0.000000e+00	0.000000	0.000000	0.000000
25%	1924.000000	0.359000	0.152000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	5.873210e+05	0.000000e+00	0.000000	0.000000	0.000000
50%	1967.000000	3.972000	1.001000	0.000000	0.000000	0.000000	0.395000	0.055000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	4.008723e+06	2.164603e+09	0.000000	0.000000	0.000000
75%	1995.000000	38.419500	4.257000	0.000000	0.510000	0.066000	12.142500	1.008500	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	1.478986e+07	3.639273e+10	9.299000	3910.669000	0.427500
max	2020.000000	36702.503000	748.639000	1028.487000	1626.371000	2.738000	15062.902000	34.184000	435.034000	94.711000	...	53.650000	8298.270000	39.795000	3078.270000	10.056000	7.794799e+09	1.136302e+14	162194.290000	317582.498000	13.493000

8 rows × 58 columns

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Who is reposible for the increase in CO2 emissions?